Provided are robotic systems and methods for navigation of luminal network that detect physiological noise. In one aspect, the system includes a set of one or more processors configured to receive first and second image data from an image sensor located on an instrument, detect a set of one or more points of interest the first image data, and identify a set of first locations and a set of second location respectively corresponding to the set of points in the first and second image data. The set of processors are further configured to, based on the set of first locations and the set of second locations, detect a change of location of the instrument within a luminal network caused by movement of the luminal network relative to the instrument based on the set of first locations and the set of second locations.

Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.

A method and system for providing and aggregating bioelectrical signal data comprising: providing a stimulus configured to prompt an action in both a first user and a second user; at a first biosignal detector and a second biosignal detector, automatically collecting a first bioelectrical signal dataset from the first user as the first user performs the action and a second bioelectrical signal dataset from the second user as the second user performs the action; generating a first anonymized bioelectrical signal dataset from the first bioelectrical signal dataset and a second anonymized bioelectrical signal dataset from the second bioelectrical signal dataset; coupling the first and the second anonymized bioelectrical signal datasets with an action tag characterizing the action; and generating an analysis based upon the first the second anonymized bioelectrical signal datasets. An embodiment of the system comprises a biosignal detector and a processor configured to implement an embodiment of the method.

Provided are a tracking method and apparatus of an exercise heart rate, a device and a storage medium. The method includes: if the user heart rate currently detected by a photoelectric heart rate device does not meet a preset heart rate standard, invoking a pre-established target model equation reflecting the relationship between a user heart rate and a user pace; and inputting the current real-time pace of a user into the target model equation to obtain the current real-time heart rate of the user. In the technical solutions, when the user heart rate currently detected by the photoelectric heart rate device does not meet the preset heart rate standard, the current real-time pace of the user is directly input into the pre-established target model equation reflecting the relationship between the user heart rate and the user pace to obtain the current real-time heart rate of the user.

A medical system is provided. The medical system includes a medical device and a mobile computing device. The medical device includes at least one physiologic sensor configured to acquire physiological signals from a patient, at least one processor coupled to the at least one physiologic sensor, and at least one optical code encoded with encrypted data. The mobile computing device includes a camera and one or more processors coupled to the camera and configured to acquire one or more images of the at least one optical code, decode the one or more images of the at least one optical code to generate a copy of the encrypted data, decrypt the copy of the encrypted data to generate decrypted data, and process the decrypted data to establish an operable connection between the mobile computing device and the medical device.

Systems and methods for late gadolinium enhancement (“LGE”) tissue viability imaging in a dynamic (e.g., temporally-resolved) manner using magnetic resonance imaging (“MRI”) are provided. Dynamic LGE images can be generated throughout the entire cardiac cycle at high temporal resolution in a single breath-hold. Dynamic, semi-quantitative longitudinal relaxation maps are acquired and retrospective synthetization of dynamic LGE images is implemented using those semi-quantitative longitudinal relaxation maps.

Disclosed apparatuses and methods apply electric fields deep in a body by selectively actuating multiple magnetic modules about the body sequentially in time. The electric field induced in regions of the body from such actuations may have a different frequency, depending on the depth of the region.

Described herein are methods and systems for extracting or determining subject motion from multi-channel electrical coupling in imaging of the subject, in particular in magnetic resonance (MR) imaging of the subject. The motion can be of a region of interest of the subject (such as an organ or specific tissue). Changes in the position of the subject and the subjects organs can be monitored by measuring how external coils, such as RF coils, couple to the subject and to one another and change the scattering of the RF coils, for example scattering of RF pulses transmitted by the coils. Changes in position influence this coupling and the scattering and can be detrimental to the quality of the imaging The present methods and systems address and overcome this problem.

Embodiments provide physiological measurement systems, devices and methods for continuous health and fitness monitoring. A wearable strap may detect reflected light from a user's skin, where data corresponding to the reflected light is used to automatically and continually determine a heart rate of the user. The wearable strap may monitor heart rate data including heart rate variability, resting heart rate, and sleep quality. The systems may include a processing module that generates an indicator of physical recovery based on the heart rate data. The recovery indicator may be used to determine strain related to an exercise routine, qualitative information on the user's health, whether to alter a user's exercise plan, and so forth.

A method of detecting an improvement in a seizure condition of a patient includes identifying a first EEG synchronization of the seizure condition of the patient; applying a therapy configured to improve the seizure condition of the patient; and identifying a second EEG synchronization of the seizure condition of the patient subsequent to application of the therapy, wherein an improvement of the seizure condition of the patient is demonstrated by a reduced EEG synchronization of the patient such that the second EEG synchronization is less than the first EEG synchronization.